Abstract: A computer-implemented method is disclosed. The computer-implemented method comprises receiving, by a computer system, ingestible event marker (IEM) system information from a receiver worn by a subject, the IEM system information comprising information associated with ingestion of medication by the subject, wherein the receiver is configured to communicate with the computer system; receiving, by the computer system, contextual information associated with the subject; and calculating, by the computer system, a composite risk score based on the IEM system information and the contextual information associated with the subject.

Abstract: A system and method are disclosed for training a system or a model to allow estimation of the value of livestock that is farmed for monetary gain. The various aspects of the invention include generation of data that is used to supplement or augment capture or real data, wherein the subject of the data is an animal. Labels or attributes are generated and validated.

Abstract: Methods, devices and systems for acquiring information useful to support a patient in implementing and adhering to a medically prescribed therapy plan are provided. The therapy may incorporate biofeedback methods and/or personalized therapy aspects. A method includes steps of receiving, by a receiving device, biometric information associated with an ingestible event marker; analyzing, by a computing device having a microprocessor configured to perform a biometric information analysis, the biometric information; and determining a therapeutic recommendation at least partly on the basis of the analysis and/or integrating biofeedback techniques into patient therapy or activity. A system includes a biometric information module to receive biometric information associated with an ingestible event marker; an analysis module to analyze the biometric information; and a determination module to optionally determine and communicate a therapeutic recommendation at least partly on the basis of the analysis.

Abstract: A system and method for training a model using a training dataset. The training dataset can be made up of only real data, only synthetic data, or any combination of synthetic data and real data. The images are segmented to define objects with known labels. The object is pasted onto backgrounds to generated synthetic datasets. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

Abstract: A system and method for training a system or a model using any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data, wherein the subject of the data can be segmented. Labels or attributes are automatically added to generated data. The generated data is synthetic data that is generated using seed or real data as well as from other synthetic data. Using the synthetic data, various domain adaptation models can be used and trained.

Abstract: A system and method are disclosed for training a system or a model using any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data, wherein the subject of the data can be segmented. Labels or attributes are automatically added to generated data. The generated data is synthetic data that is created using the seed or real data as well as from other synthetic data. Using the synthetic data, various domain adaptation models can be used and trained for unsupervised domain adaptation.

Abstract: A system and method are disclosed for training a model using a training dataset. The training dataset can include only real data, only synthetic data, or any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

Abstract: The ingestible event marker data framework provides a uniform, comprehensive framework to enable various functions and utilities related to ingestible event marker data (IEM data). The functions and utilities include data and/or information having an aspect of data derived from, collected by, aggregated by, or otherwise associated with, an ingestion event.

Abstract: The ingestible event marker data framework provides a uniform, comprehensive framework to enable various functions and utilities related to ingestible event marker data (IEM data). The functions and utilities include data and/or information having an aspect of data derived from, collected by, aggregated by, or otherwise associated with, an ingestion event.

Abstract: The ingestible event marker data framework provides a uniform, comprehensive framework to enable various functions and utilities related to ingestible event marker data (IEM data). Included are a receiver adapted to be associated with a body of an individual, the receiver configured to receive IEM data; a hub to receive the IEM data; and at least one IEM data system to receive the data from the hub. Among other information, behavioral data and predictive inferences may be provided.

Abstract: A system and method are disclosed that track a deliverable to a user. The system includes an identifier or tag secured to the deliverable, a computer system for interrogating the identifier, and a personal device in communication with the computer system, wherein the personal device is held by the user at the time the user is administered the deliverable to detect the unique identity associated with the identifier device and confirms delivery of the deliverable to the user. The method includes attaching an identifiable tag that produces a unique signature to the deliverable, interrogating the tag at about the time of delivery to the user, and confirming that the user has been administered the deliverable through detecting the identifiable tag.

Abstract: A system and method are disclosed for training a system or a model using any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data, wherein the subject of the data can be segmented. Labels or attributes are automatically added to generated data. The generated data is synthetic data that is generated using seed or real data as well as from other synthetic data. Using the synthetic data, various domain adaptation models can be used and trained.

Abstract: A system and method are disclosed for training a model using a training dataset. The training dataset can include only real data, only synthetic data, or any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

Abstract: A system and method are disclosed for training a system or a model using any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data, wherein the subject of the data can be segmented. Labels or attributes are automatically added to generated data. The generated data is synthetic data that is created using the seed or real data as well as from other synthetic data. Using the synthetic data, various domain adaptation models can be used and trained for unsupervised domain adaptation.

Abstract: A system and method are disclosed for training a model using a training dataset. The training dataset can be made up of only real data, only synthetic data, or any combination of synthetic data and real data. The images ae segmented to define objects with known labels. The object is pasted onto backgrounds to generated synthetic datasets. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

Abstract: A monitoring system for generating a mental state indicator for use in identifying a mental state of a biological subject, including one or more electronic processing devices that obtain subject data indicative of at least a heart rate measured for the biological subject during at least part of a sleep episode, analyze the subject data to determine at least one sleep segment; analyze the subject data to determine at least one metric for the at least one sleep segment, and apply the at least one metric to a computational model to determine a mental state indicator indicative of a mental state, the computational model embodying a relationship between different mental states and one or more metrics, and being obtained by applying machine learning to reference metrics derived from heart rates measured for one or more reference subjects during at least part of a reference sleep period.

Abstract: A system and method are disclosed for adjusting a future feeding cycles based on analysis of feed consumption during and at the end of a current feed cycle. The system and method are applicable to any environment where animals are fed, including containers that hold feed and containers that hold the animal and the feed, such as a fish tank.

Abstract: A system and method are disclosed for training a system or a model to allow estimation of the value of livestock that is farmed for monetary gain. The various aspects of the invention include generation of data that is used to supplement or augment capture or real data, wherein the subject of the data is an animal. Labels or attributes are generated and validated.

Abstract: The disclosed system includes an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a signal including the IEM data, wherein the IEM data include information associated with an ingestion event, a receiver adapted to be associated with the body of the individual and configured to receive the signal including the IEM data via the body of the individual, a hub to receive the IEM data from the receiver, and at least one IEM data system to receive the IEM data from the hub. The at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data. The at least one IEM data system may further generate predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual.

Abstract: A system and method are disclosed that track a deliverable to a user. The system includes an identifier or tag secured to the deliverable, a computer system for interrogating the identifier, and a personal device in communication with the computer system, wherein the personal device is held by the user at the time the user is administered the deliverable to detect the unique identity associated with the identifier device and confirms delivery of the deliverable to the user. The method includes attaching an identifiable tag that produces a unique signature to the deliverable, interrogating the tag at about the time of delivery to the user, and confirming that the user has been administered the deliverable through detecting the identifiable tag.